Objective.Adaptive deep brain stimulation (aDBS) for Parkinson's disease is a recently-approved therapy that adjusts stimulation in response to neurophysiologic biomarkers of motor-symptom state. Most real-time implementations of aDBS rely on instantaneous, noise-susceptible classifiers that apply simple thresholds to neurophysiologic biomarkers. We examined whether incorporating temporal history through Bayesian state-space modeling improved motor-state classification compared to instantaneous discriminant classifiers.Approach. We analyzed naturalistic neural data from three patients with Parkinson's disease chronically implanted with investigational sensing-enabled DBS systems, recording from both the subthalamic nucleus (STN) and sensorimotor cortex. Biomarkers were extracted across multiple window lengths and labeled using wearable-derived bradykinesia and dyskinesia scores. Classifier behavior was evaluated using two biomarkers (cortical stimulation-entrained gamma and STN beta oscillations) across a factorial combination of two conditions: (1) instantaneous discriminant analysis vs Bayesian time-history modeling via hidden Markov models (HMMs), and (2) single Gaussian vs Gaussian mixture modeling of each motor state's biomarker distribution. Performance metrics includedF1 scores, accuracy, prediction smoothness, latency, and computational load.Main Results. Using entrained-gamma biomarkers, incorporating time history via HMMs significantly improved hyperkinetic-state detection (F1: +12.9 ± 1.8%; accuracy: +30.0 ± 2.7%; bothpadj< 0.001) with modest decreases in hypokinetic-state performance, yielding a net increase in averageF1 (+4.7 ± 0.9%,p< 0.001). HMMs also yielded smoother and more accurate predictions for a given latency compared to simply increasing the window length used to extract neurophysiologic biomarkers. Entrained-gamma biomarkers outperformed STN beta biomarkers across all classifiers (averageF1: +12.9% ± 0.5%,p< 0.001). All methods operated within sub-millisecond prediction times and demonstrated sublinear empirical computational scaling.Significance. Bayesian time-history modeling enhanced motor-state classification while preserving the low latency and computational efficiency required for real-time aDBS. These findings, derived from chronic at-home recordings, support the translational potential of Bayesian state-space models for next-generation aDBS systems.

Objective.Accurate localization of the seizure onset zone (SOZ) is critical for successful surgical intervention in drug-resistant epilepsy. high frequency oscillation (HFO)-based SOZ localization methods often exhibit limited accuracy, which constrains their ability to reliably track large-scale and dynamic brain network activity. Microstate analysis captures dynamic spatiotemporal patterns of epileptogenic networks, improving localization. Propagation zone electrodes can mimic SOZ activity and complicate mapping. This study aims to enhance SOZ precision by combining microstate analysis with a Neuro Rank scoring system to eliminate false positives including propagation zone electrodes while prioritizing true SOZ regions.Approach.We developed a framework integrating HFO detection, microstate analysis, and Neuro Rank-based propagation zone refinement. HFOs were detected and characterized using time-frequency features and clustered via fuzzy C-means. Microstate analysis identified dominant spatiotemporal patterns using global field power (GFP) peaks. Electrodes identified by both methods were refined using Neuro Rank, which integrates PageRank (PR), reverse PR, and hyperlink-induced topic search to down-weight propagation zone electrodes while highlighting true SOZ activity.Main results.Using a leave-one-patient-out evaluation, the proposed pipeline combining HFO, microstate features, and Neuro Rank achieved the best performance on the Freiburg dataset with 95.71% accuracy, 74.83% sensitivity, 98.56% specificity, and an Area Under the receiver operating characteristic Curve (AUC) of 0.83, while demonstrating strong generalization on the external HUP dataset with 96.34% accuracy, 72.34% sensitivity, 97.88% specificity, and an AUC of 0.80.Significance.Combining local HFO biomarkers, microstate-derived network dynamics, and Neuro Rank suppression of propagation zone electrodes provides precise and reliable SOZ localization, supporting improved surgical planning and outcomes in drug-resistant epilepsy.Terms-SOZ localization, intracranial EEG, HFOs, EEG microstate analysis, Neuro Rank graph score.

Objective.The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to efficiently estimate a signal's Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data.Approach.This work uses the inverse compression ratio (ICR) to analyze recordings (Utah arrays) taken from motor cortex of animals performing reaching tasks three days before and three days after administering electrolytic lesions (SubjectU: 4 lesions,H: 3). We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data (average firing rates and Fano factor), as well as common dimensionality reduction techniques (principal component analysis and factor analysis).Main Results.ICR is able to significantly (Mann-Whitney U test,p<0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and 'optimal' compression on lesion detection performance. Simulated data shows that ICR is computationally advantageous.Significance.These results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.

Objective.Electroencephalogram (EEG) signal variability caused by external factors and subject differences limits the adaptation of motor imagery (MI) classification models in brain-computer interfaces (BCIs). Existing domain alignment methods often inadequately utilize critical source and target domains information, leading to negative transfer problems. This paper proposes a Feature Alignment and Enhancement Framework for cross-domain MI-EEG classification to address these limitations.Approach.First, by aligning the covariance matrices of the source and target domains, the spatial distributions of the two domains are preliminarily aligned, establishing a consistent foundation for feature mapping. Second, a conditional domain adversarial network optimizes cross-domain representations, reducing distribution discrepancies while enhancing discriminability. Finally, this paper introduces an EEG feature-based guided tuning method. This method extracts high-confidence features from both the source and target domains and generates centroid features to construct cross-domain feature banks. The input feature representations are dynamically optimized by attending to the relationships between centroid features, thus enhancing the model's adapt-ability to target domain tasks.Main results.Experimental data show that in the four-class MI task of the BCI Competition IV-2a dataset, the cross-session and cross-subject model classification accuracies were 76.89% and 57.91%, respectively. The model achieved accuracy rates of 84.61% and 82.78% on the BCI Competition IV-2b datasets and the High Gamma Datasets, respectively, as well as 84.09% and 70.81%.Significance.The proposed framework effectively mitigates cross-domain variations, providing a reliable solution for cross-session and cross-subject MI-EEG classification.

Objective.High frequency oscillations (HFOs) are a promising biomarker of epilepsy, but automated detectors have significant risk for false positives due to diverse EEG artifacts. Many of these artifacts, previously uncharacterized in HFO research, are readily visible to clinicians under standard EEG viewing conditions. We present an artifact detector trained on clinician markings that identify when HFOs were produced by artifacts.Approach.Clinicians read standard resolution (10 sec per screen, all channels visible, 0-30 Hz), intracranial EEG with 8000 HFO events marked in 35 patients. They labeled each event as brain-derived or artifactual based upon their expert interpretation of the EEG at the time of the event, creating a new gold standard of HFO labeling. We used 4000 events for training/validation and 4000 for held-out prospective testing. We extracted features at the time of the HFOs from the single intracranial HFO channel and the scalp and intracranial common average reference, then trained candidate supervised learning classifiers to distinguish artifacts and non-artifactual HFOs (naHFOs).Main results.The resulting Michigan Intracranial Artifact Filter (MIAF) uses binary logistic regression on just intracranial data at the time of the HFO detection to remove false positives. The MIAF applied on held-out patient data significantly increased positive predictive value from 86% to 98% and resulted in an area under the precision recall curve and receiver operating characteristic curve of 99% and 92% respectively. It improved the correlation between HFOs and the seizure onset zone and resected volume in 76.5% and 88.9% of patients respectively, outperformed alternative artifact detectors, and allowed HFO analysis from all states of vigilance.Significance.MIAF effectively removes false positives HFO detections while retaining sufficient naHFOs for downstream analysis. Because it relies only on raw intracranial data during HFO detections, it can be easily ported to other HFO detectors and recording environments.

Spatial memory serves as a foundation to establish cognitive map, supporting navigation and decision-making processes across species. Essential brain regions such as the hippocampus and entorhinal cortex enable these functions through spatially tuned neurons, particularly place cells, which encode an animal's precise location. The continuous spatial trajectories are then able to be represented by temporally sequential firing of these cells at neural ensemble level. Bayesian frameworks are powerful tools for reconstructing such 'mind travel'. In this article, we focus on the principles and advances of Bayesian decoding methods for extracting spatial memory information from neural ensembles. First, we review non-recursive approaches and recursive point process filters, paying special attention to clusterless decoding strategies. We also discuss emerging approaches such as neural manifolds within Bayesian estimation. Next, we discuss the advanced application of Bayesian decoding in understanding the neuronal coding mechanisms of memory consolidation and planning, and in supporting computational model establishment and closed-loop manipulation. Finally, we discuss the limitations and challenges of recent approaches, highlighting the promising strategies that could raise the decoding efficiency and adapt the growing scale of neural data. We believe that the developing of Bayesian decoding approach would significantly benefit for techniques and applications of memory-related brain machine interface.

Objective.Functional near-infrared spectroscopy (fNIRS) is an emerging technique for brain-computer interfaces (BCIs) due to its advantages in spatial resolution, robustness to artifacts, portability and usability for long-term monitoring, etc. Existing BCI methods take a holistic approach to all signal-collecting channels and corresponding brain regions, while the task-related brain regions and their interactions have not been well explored.Approach.This paper proposes a principal brain-region analysis (PBA) framework to incorporate the functional contribution as well as collaboration of task-specific brain regions (TSBRs) to boost BCI performance. Firstly, the identification of TSBRs is formulated as an optimization problem by maximizing classification accuracy under spatial constraints on brain regions of interest. Then, an evolutionary decomposition algorithm is constructed by combining spatial nondominated operators and genetic iterative computation, identifying TSBRs from the whole brain regions. Afterwards, classifiers are trained by neuroimaging features in the decomposed TSBRs in combination with stacking to generate the final predictions.Main results.The proposed PBA method was evaluated on two public datasets for fNIRS-based BCIs, significantly enhancing the classification accuracy for the sliding slope-based method by 8.91% and 6.03% and the sliding mean concentration change method by 13.62% and 6.15%, respectively.Significance.PBA establishes a pivotal framework to fundamentally advance the accuracy and explainability of BCIs.

Objective.Neuroprosthetic devices require multichannel stimulator systems with an increasing number of channels. However, there are inherent power losses in typical multichannel stimulation circuits caused by mismatches between the power supply voltage and the voltage required at each electrode to successfully stimulate tissue. This imposes a bottleneck towards high-channel-count devices, which is particularly severe in wirelessly-powered devices. Hence, advances in the power efficiency of stimulation systems are critical. To support these advances, this paper presents a methodology to identify and quantify power losses associated with different power supply scaling strategies in multichannel stimulation systems.Approach.The methodology uses distributions of stimulation amplitudes and electrode impedances to calculate power losses in multichannel systems. Experimental data from prior studies spanning various stimulation applications were analyzed to evaluate the performance of fixed, global, and stepped supply scaling methods, focusing on their impact on power dissipation and efficiency.Main Results.Variability in output conditions results in low power efficiency in multichannel stimulation systems across all applications. Stepped voltage scaling demonstrates substantial efficiency improvements, achieving an increase of 43% to 100%, particularly in high-channel-count applications with significant variability in tissue impedance. In contrast, global scaling proved effective only in systems with fewer channels and minimal inter-channel variation.Significance.The findings highlight the importance of tailoring power management strategies to specific applications to optimize efficiency while minimizing system complexity. The proposed methodology provides a framework for evaluating trade-offs between efficiency and system complexity, facilitating the design of more scalable and power-efficient neurostimulation systems.

Objective.Peripheral nerve stimulation (PNS) offers therapeutic benefits across numerous clinical applications but remains limited by poor spatial selectivity in mixed nerves. This study aimed to evaluate whether a fully polymeric, transverse, multipolar nerve cuff can achieve selective fascicular activation and to assess the predictability of such selectivity using imaging-informed computational models.Approach.A flexible, fully polymeric nerve cuff fabricated from a conductive elastomer (CE) was developed and evaluatedex vivoon rat sciatic nerves. Compound nerve action potentials were recorded from individual fascicles to quantify selectivity across a wide range of stimulation parameters. The non-metallic electrodes enabled microCT-based three-dimensional reconstruction of nerve-electrode geometries without imaging artifacts, which were incorporated into anatomically accurate simulations using the ASCENT modeling pipeline.Main results. Ex vivoexperiments demonstrated reliable neural recordings and high levels of fascicular selectivity (selectivity index > 0.65 in each fascicle). Imaging-informed simulations reproduced selective activation patterns in some cases but showed systematic discrepancies in both selectivity magnitude and electrode-fascicle correspondence, particularly for sural and tibial fascicles. Simulated outcomes were more sensitive to neuroanatomical variability than experimental results, highlighting limitations in current modeling assumptions.Significance.These findings validate fully polymeric CE cuffs as effective alternatives to metallic nerve interfaces for selective PNS. The study also demonstrates the value of combining microCT imaging with computational modeling to interrogate and refine predictive frameworks, underscoring the need for improved tissue and electrode modeling to advance spatially selective PNS technologies.

Objective. The efficacy of neuromodulation techniques like transcranial magnetic stimulation (TMS), are highly dependent on the geometry and conductivity of the stimulated target of the brain. Although anatomically accurate head models are routinely used for computational simulations of TMS-induced electric (E) fields, there is still a need for realistic phantoms that mimic the anatomy and electrical conductivity of the head.Approach.Here, we present a realistic rat brain phantom constructed using advanced 3-d printing technologies. Our phantom allows validation of TMS-induced E-fields using embedded mutually orthogonal triaxial dipole probes (TDP) that can measure induced E-fields along three axes. We tested the TDP probes in the constructed phantom using four TMS coils with different core materials and core geometry. These measurements were then compared to computational simulations using the finite element method (FEM).Main results.The rat brain phantoms had a conductivity of roughly 0.5 S m-1, which was mimicked in the FEM simulations. When the measured induced E-fields in the phantoms were compared to the simulated results, the measured results were in the same expected range and fairly close to one another with an average error of 5.1%. The peak E-fields (measured vs. simulated) close to the surface of the gray matter were: permender v-tip core (115.3-110 V m-1), permender flat-tip core (91.9-85 V m-1), AISI 1010 v-tip core (94.7-100 V m-1), and AISI 1010 flat-tip core (85.9-84 V m-1) respectively.Significance.These anatomically accurate and conductive rat head phantoms may be used in some cases to replacein-vivoexperimentation and computational simulations reducing regulatory requirements and lengthy simulation times.